Predicting an Outcome Using Regression Models

  • Instructions

    Hospital administration needs to make a decision on the amount of reimbursement required to cover expected costs for next year. For this assessment, using information on hospital discharges from last year, perform multiple regression on the relationship between hospital costs and patient age, risk factors, and patient satisfaction scores, and then generate a prediction to support this health care decision. results.

    Grading Criteria

    1. Perform the appropriate multiple regression using a dataset.
    2. Interpret the statistical significance and effect size of the regression coefficients of a data analysis.
      • Interpret p-value and beta values.
    3. Interpret the fit of the regression model for prediction of a data analysis.
      • Interpret R-squared and goodness of fit.
    4. Apply the statistical results of the multiple regression of a data analysis to support a health care decision.
      • Generate a prediction with regression equation.
    5. Write a narrative summary of the results that includes practical, administration-related implications of the multiple I already ran all of the correlation and regression analytics. My problem is I don’t know why when I did a correlation analysis the data indicated that satisfaction and risk had no significant correlation, however when I ran a regression model it showed that risk did have a significant correlation. I see that this assignment was already submitted for explanation and an answer. Not sure how I go about seeing it. I have attached my calculations.

Requirements: Short, I can elaborate once I understand what is hapening

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